Abstract
Engineering applications of finite volume Shallow-water Hydro-Sediment-Morphodynamic models (SHSM) have faced limitations due to their high computational demands arising from either extremely large amounts of computational cells or extremely small time steps at some regions and simultaneously the adoption of the globally minimum time step. To this end, we present an engineering-oriented modeling framework by (1) using the GPU-acceleration that overcomes the challenge of extremely large amounts of computational cells and (2) using a hybrid local-time-stepping/global maximum time step (LTS/GMaTS) strategy that mitigates the extremely small local time steps necessitated by locally-refined meshes or non-uniformity of flow conditions. The GPU parallel algorithm is tailored to fully leverage the computational power of GPU, optimizing numerical structure, kernel functions and memory usage, all in conjunction with the hybrid LTS/GMaTS implementation. We demonstrate its computational efficiency by simulating one experimental dam-break flow and a field-scale case in the Xinjiu waterway, Middle Yangtze River. The results show that the scheme performs well in terms of accuracy, efficiency, and robustness in reproducing real-world hydro-sediment-morphological evolution.
Published Version
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